Silhouette-based object and texture alignment, systems and methods

ABSTRACT

An object-image alignment data generating method for use in an object recognition system is presented. The method obtains a 3D model and a set of 2D images of the object. Each 2D image from the set is captured based on a particular camera point of view. The method then uses the 3D model of the object to generate multiple silhouettes of the object according to different camera point of views. Each silhouette is then matched and aligned with a 2D image based on the corresponding camera point of view. The method also derives at least one descriptor from the 2D images and compiles feature points that correspond to the descriptors. Each feature point includes a 2D location and a 3D location. The method then generates an object-image alignment packet by packaging the 2D images, the descriptors, and the feature points.

This application claims priority to U.S. Application 61/905,575, filedNov. 18, 2013. This and all other extrinsic materials discussed hereinare incorporated by reference in their entirety. Where a definition oruse of a term in an incorporated reference is inconsistent or contraryto the definition of that term provided herein, the definition of thatterm provided herein applies and the definition of that term in thereference does not apply.

FIELD OF THE INVENTION

The field of the invention is object recognition technology.

BACKGROUND

The following description includes information that may be useful inunderstanding the present invention. It is not an admission that any ofthe information provided herein is prior art or relevant to thepresently claimed invention, or that any publication specifically orimplicitly referenced is prior art.

Consumers continue to experience an increasingly blurred distinctionbetween real-world and on-line interactions. With the advent of objectrecognition technologies available today, consumers can now virtuallyinteract with real-world objects through their smart phones. Forexample, consumers can capture an image of a movie poster via their cellphones. In response, the cell phone can construct an augmented realityinteraction or game overlaid on the display of the cell phone. In fact,the Applicant has pioneered such technologies through their iD®technologies as implemented by DreamPlay™ (see URLwww.polygon.com/2013/1/9/3851974/disney-dreamplay-ar-app-disney-infinity)Other technologies that attempt to offer similar experiences include thefollowing:

-   -   Layar® (see URL www.layar.com),    -   Qualcomm Vuforia™ (see URL        www.qualcomm.com/solutions/augmented-reality)    -   BlippAR.com™ (see URL www.blippar.com), and    -   13th Lab (see URL www.13thlab.com).

Unfortunately, such technologies are limited in scope and typically areonly capable of recognizing a single object at a time (e.g., a singletoy, a single person, a single graphic image, etc.). In addition, aconsumer must position their cell phone into a correct position ororientation with respect to the object of interest, then wait for theirthe cell phone to analyze the image information before engaging contentis retrieved. Ideally a consumer should be able to engage contentassociated with an object of interest very quickly and should be able toengage many objects at the same time. The above referenced companiesfail to provide such features.

Other efforts have been made in the field of object recognition. Forexample, in the publication “Silhouette-Based Object PhenotypeRecognition Using 3D Shape Priors” by Chen et al., published in the 2011IEEE International Conference on Computer Vision, Nov. 6-13, 2011, Chenstates that there is a fundamental problem in recognizingthree-dimensional (3D) objects from one or more two-dimensional (2D)views in computer vision. However, Chen takes a computationallyintensive approach of generating large numbers of possible poses.Unfortunately, such implementations are not suitable for mobile handhelddevices and merely attempt to view shape as an identifier. Chen pointsout numerous deficiencies with respect to recognizing 3D objects.

U.S. Pat. No. 6,858,826 “Method and Apparatus for ScanningThree-Dimensional Objects” issued to Mueller et al., filed Aug. 13,2002, also recognizes the difficulty of recognizing 3D objects. Muellerspecifically points out the difficulty of prior techniques that scan for2D color information and separately scan for 3D information. Muellerrather uses a series of 2D color images to derive 3D points in space.However, such an approach fails to provide scale invariance whenconducting recognition in handheld devices.

U.S. Pat. 6,954,212 “Three-Dimensional Computer Modeling” issued toLyons et al., filed Nov. 5, 2002, describes building a 3D computer modelof an object by aligning image data with silhouettes of computergenerated model. Although Lyon discloses adequate building of 3D models,such modeling information is not practical for full 3D objectrecognition or tracking on resource-constrained devices.

U.S. Pat. No. 7,728,848 “Tools for 3D Mesh and Texture Manipulation”issued to Petrov et al., filed Mar. 28, 2001, teaches a method forediting three dimensional computer models and textures that providesmore precisely selecting portions of the model for editing, allowingtextures to be moved more easily on the model and allowing betterblending of the appearance of adjacent textures.

U.S. Patent Publication 2006/0232583 “System and Method ofThree-Dimensional Image Capture and Modeling” to Petrov et al., filedMay 30, 2006, teaches a system for constructing a 3D model of an objectbased on a series of silhouette and texture map images.

U.S. Patent Publication 2011/0007072 “Systems and Methods forThree-Dimensionally Modeling Moving Objects” to Khan et al., filed Jul.9, 2009, describes building a 3D model by first capturing images of anobject from different viewpoints, identifying silhouettes of the objectin each viewpoint, and then identifying the silhouette boundary pixels.

U.S. Patent Publication 2013/0188042 “System and Method for ObjectMeasurement” to Brooksby, filed Mar. 12, 2013, describes building amodel of an object by combining 2D images with a 3D CAD model. Theobjects are built by linking images with point correspondences frommodel parameters.

U.S. Patent Publication 2008/0143724 “Method and Apparatus forProbabilistic Atlas Based on Shape Modeling Technique” to Russakoff,filed Dec. 19, 2006, describes generating shape models of breasts basedon silhouettes. Control points are placed along the edges of atwo-dimensional breast silhouette and are used for deformational imageanalysis during mammogram viewing by comparing the control points placedon a baseline breast silhouette and the control points placed on anupdated breast silhouette.

However, none of the references mentioned above provides an accurate 3Dobject recognition technique that is not computationally intensive,allowing real-time tracking of recognized objects. Thus, there is stilla need to improve upon conventional 3D object recognition techniques.

All publications herein are incorporated by reference to the same extentas if each individual publication or patent application werespecifically and individually indicated to be incorporated by reference.Where a definition or use of a term in an incorporated reference isinconsistent or contrary to the definition of that term provided herein,the definition of that term provided herein applies and the definitionof that term in the reference does not apply.

SUMMARY OF THE INVENTION

The inventive subject matter provides apparatus, systems and methods ofgenerating object-image alignment data for different 3D objects. Oncegenerated, the object-image alignment data can be sent to an objectrecognition application for use in recognizing or tracking real-worldobjects. In some embodiments, the object recognition application isbeing run on a mobile device having an image capturing device forcapturing images of real world objects. The object recognitionapplication recognizes real world objects in the images by comparing theimages with the object-image alignment data.

In some embodiments the method of generating the object-image alignmentdata includes a step of obtaining a digital three-dimensional (3D) modelof a 3D object. The method also includes a step of obtaining a set oftwo-dimensional (2D) images of the object from several image viewpoints. The method includes a step of generating a silhouette of theobject from the digital 3D model. The silhouette includes a collectionof edge points associated with edges of the digital 3D model from asilhouette point of view.

The method also includes a step of registering at least one image fromthe set of 2D images with the silhouette based on image view pointassociated with the at least one image and the silhouette point of view.The method includes a step of deriving at least one descriptor from theat least one image, and compiling feature points corresponding to the atleast one descriptor based on the at least one image and the 3D model.The feature points includes at least one 2D location and at least one 3Dlocation of the at least one descriptor within the model. The methodincludes the step of generating an object-image alignment packet bypackaging the at least one image, the at least one descriptor, and thefeature point.

The digital 3D model of the object can be obtained in different ways. Insome embodiments, the digital 3D model of the object is obtained bylaser scanning the object. In other embodiments, the digital 3D modelcan be obtained by obtaining CAD data representing the object. In yetsome other embodiments, the digital 3D model can also be obtained byobtaining a game engine asset, such as unity3D® or OpenGL, thatrepresents the object.

The set of 2D images of the object can be obtained in many differentways as well. In some embodiments, the set of 2D images includes imagedata captured via an optical sensor. The image data can include stillimage data or a frame from a video stream. The image data can alsoinclude video data and/or data related to the properties of the opticalsensor that captured the image data.

In some embodiments, the collection of points of the silhouette includes3D points within the 3D model. In some of these embodiments, thecollection of points includes relative 3D coordinates to a camera pointof view. In some embodiments, each feature point also includes a set of3D coordinates relative to a camera point of view.

In some embodiments, registering at least one image from the set of 2Dimages with the silhouette requires aligning a portion of the image dataof the object within the at least one image to at least some of the edgepoints. Specifically, aligning the image data means aligning edge pixelswithin the image data of the object to at least some of the edge pointsof the silhouette.

The at least one descriptor derived from the image can be an imagedescriptor selected from the following different types of imagedescriptors: a SIFT descriptor, a DAISY descriptor, a FREAK descriptor,a FAST descriptor, or other type of descriptor.

In some embodiments, the object-image alignment packet also includes akey frame packet. The object-image alignment packet can also include atleast one of the following: a normal vector, an orientation, sensormetadata, and other key frame data.

After generating the object-image alignment packet, the method can alsoinclude a step of sending the object-image alignment packet to anelectronic device (e.g., a mobile device) over a network.

Various objects, features, aspects and advantages of the inventivesubject matter will become more apparent from the following detaileddescription of preferred embodiments, along with the accompanyingdrawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary object modeling system of someembodiments.

FIG. 2 is a flow chart of a process according to some embodiments of theinventive subject matter.

FIG. 3 illustrates how silhouettes are aligned with 2D images accordingto some embodiments of the inventive subject matter.

FIG. 4 illustrates how descriptors are derived from a 2D image accordingto some embodiments of the inventive subject matter.

DETAILED DESCRIPTION

Throughout the following discussion, numerous references will be maderegarding servers, services, interfaces, engines, modules, clients,peers, portals, platforms, or other systems formed from computingdevices. It should be appreciated that the use of such terms is deemedto represent one or more computing devices having at least one processor(e.g., ASIC, FPGA, DSP, x86, ARM, ColdFire, GPU, multi-core processors,etc.) configured to execute software instructions stored on a computerreadable tangible, non-transitory medium (e.g., hard drive, solid statedrive, RAM, flash, ROM, etc.). For example, a server can include one ormore computers operating as a web server, database server, or other typeof computer server in a manner to fulfill described roles,responsibilities, or functions. One should further appreciate thedisclosed computer-based algorithms, processes, methods, or other typesof instruction sets can be embodied as a computer program productcomprising a non-transitory, tangible computer readable media storingthe instructions that cause a processor to execute the disclosed steps.The various servers, systems, databases, or interfaces can exchange datausing standardized protocols or algorithms, possibly based on HTTP,HTTPS, AES, public-private key exchanges, web service APIs, knownfinancial transaction protocols, or other electronic informationexchanging methods. Data exchanges can be conducted over apacket-switched network, a circuit-switched network, the Internet, LAN,WAN, VPN, or other type of network.

One should appreciate that the disclosed authentication system providesnumerous advantageous technical effects. The system enables computingdevices to exchange digital tokens in the form of highly complex digitalimage descriptors derived from digital image data. The digital tokensare exchanged over a network as part of an authentication handshakefunction. If the computing device determines that the image descriptorssatisfy authentication criteria, then the devices are consideredauthenticated. Thus, multiple computing devices are able to establishtrusted communication channels among each other.

The following discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

As used herein, and unless the context dictates otherwise, the term“coupled to” is intended to include both direct coupling (in which twoelements that are coupled to each other contact each other) and indirectcoupling (in which at least one additional element is located betweenthe two elements). Therefore, the terms “coupled to” and “coupled with”are used synonymously.

In some embodiments, the numbers expressing quantities of ingredients,properties such as concentration, reaction conditions, and so forth,used to describe and claim certain embodiments of the inventive subjectmatter are to be understood as being modified in some instances by theterm “about.” Accordingly, in some embodiments, the numerical parametersset forth in the written description and attached claims areapproximations that can vary depending upon the desired propertiessought to be obtained by a particular embodiment. In some embodiments,the numerical parameters should be construed in light of the number ofreported significant digits and by applying ordinary roundingtechniques. Notwithstanding that the numerical ranges and parameterssetting forth the broad scope of some embodiments of the inventivesubject matter are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable. Thenumerical values presented in some embodiments of the inventive subjectmatter may contain certain errors necessarily resulting from thestandard deviation found in their respective testing measurements.

As used in the description herein and throughout the claims that follow,the meaning of “a,” “an,” and “the” includes plural reference unless thecontext clearly dictates otherwise. Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise.

Unless the context dictates the contrary, all ranges set forth hereinshould be interpreted as being inclusive of their endpoints andopen-ended ranges should be interpreted to include only commerciallypractical values. The recitation of ranges of values herein is merelyintended to serve as a shorthand method of referring individually toeach separate value falling within the range. Unless otherwise indicatedherein, each individual value within a range is incorporated into thespecification as if it were individually recited herein. Similarly, alllists of values should be considered as inclusive of intermediate valuesunless the context indicates the contrary.

All methods described herein can be performed in any suitable orderunless otherwise indicated herein or otherwise clearly contradicted bycontext. The use of any and all examples, or exemplary language (e.g.“such as”) provided with respect to certain embodiments herein isintended merely to better illuminate the inventive subject matter anddoes not pose a limitation on the scope of the inventive subject matterotherwise claimed. No language in the specification should be construedas indicating any non-claimed element essential to the practice of theinventive subject matter.

Groupings of alternative elements or embodiments of the inventivesubject matter disclosed herein are not to be construed as limitations.Each group member can be referred to and claimed individually or in anycombination with other members of the group or other elements foundherein. One or more members of a group can be included in, or deletedfrom, a group for reasons of convenience and/or patentability. When anysuch inclusion or deletion occurs, the specification is herein deemed tocontain the group as modified thus fulfilling the written description ofall Markush groups used in the appended claims.

As used in the description herein and throughout the claims that follow,when a system, engine, or a module is described as configured to performa set of functions, the meaning of “configured to” or “programmed to” isdefined as one or more processors being programmed by a set of softwareinstructions to perform the set of functions.

The focus of the disclosed inventive subject matter is to enableconstruction or configuration of a computing device to operate on vastquantities of digital data, beyond the capabilities of a human. Althoughthe digital data represents a real-world object, it should beappreciated that the digital data is a representation of one or moredigital models of the real-world object, not the real-world objectitself. By instantiation of such digital models in the memory of thecomputing devices, the computing devices are able to manage the digitaldata or models in a manner that could provide utility to a user of thecomputing device that the user would lack without such a tool.

The inventive subject matter provides apparatus, systems and methods ofgenerating object-image alignment data for different 3D objects. Oncethe object-image alignment data is generated, the data can be sent to anobject recognition application for use in recognizing or trackingreal-world objects. In some embodiments, the object recognitionapplication is being run on a mobile device having an image capturingdevice for capturing images of real world objects. The objectrecognition application recognizes real world objects in the images bycomparing the images taken by the mobile device against the object-imagealignment data.

FIG. 1 illustrates an example object modeling system 100 for generatingsuch object-image alignment data. The modeling system 100 includes anobject modeling manager 105, an object data interface 110, a silhouettegeneration module 115, a descriptor module 120, an alignment module 125,and a network interface 130. In some embodiments, the object modelingmanager 105, the object data interface 110, the silhouette generationmodule 115, the descriptor module 120, the alignment module 125, and thenetwork interface 130 are implemented as software modules that areexecutable by at least one processing unit (e.g., a processor, aprocessing core) of a computing device. In some embodiments, thesedifferent modules and interfaces can be implemented across multiplephysical devices to form the object modeling system 100.

In some embodiments, the object modeling system 100 may becommunicatively coupled with an object-image alignment packets database135. The database 135 is a permanent data storage device that can beimplemented on non-transitory devices such as a hard drive, a flashmemory, etc. In some embodiments, the object-image alignment packetsdatabase 135 is implemented within the same computing device(s) havingthe object modeling system 100. In other embodiments, the object-imagealignment packets database 135 may be communicatively coupled with theobject modeling system 100 over a network (e.g., a Local Area Network(LAN), a Wide Area Network (WAN), the Internet, etc.).

As shown in FIG. 1, the object modeling system 100 can also becommunicatively coupled with several other devices to receive objectdata. These devices are configured to capture data related to theappearance of real-world 3D objects and feeding the data to the objectmodeling system 100 via the object data interface 110. Examples of thesedata capturing devices can include a laser 3D scanner, a camera, acomputing device running a computer-aided design (CAD) software.

Different data capturing devices provide different types of data for theobject modeling system 100. For example, a laser 3D scanner 140 cancapture data to generate a digital 3D model of the object representingthe three-dimensional surface (i.e., the shape and contour) of theobject. The digital 3D model includes coordinates information that whenrendered by a computing device, can be expressed as a graphicalrepresentation of the object. In some embodiments, a laser 3D modelincludes a collection of points in a three-dimensional space, connectedby various geometric entities such as triangles, polygons, lines, curvedsurfaces, edges or other entities to represent the 3D nature of theobject. In order to capture this collection of “points”, the object isfirst coated with a special material (e.g., paint, coatings, primers,etc.), and then undergo one or more laser scans to capture a set ofcoordinates in the 3D space. The coordinate data is passed to a 3Dmodeling software to generate a 3D model of the object. The 3D model canbe rendered to be displayed on a display screen for users to view, andcan also be compared with other 3D or 2D images for matching.

In some embodiments, CAD software can be used to assist in creating the3D model of the object. In addition, the digital 3D model can also bebuilt using a game engine asset such as Unity3D® or OpenGL.

As shown in FIG. 1, a set of lasers 140 are used to scan the body of anobject 150, a bottle in this example, after coating a layer ofnon-reflective paint on the object 150. The data is fed into a computingdevice 155 running a CAD program. As shown, a rendered 3D presentationis shown on the display of the computing device 155 using data receivedfrom the 3D laser scan.

Image capturing devices such as cameras 145 can also be used to capturedata of the 3D object. In some embodiments, multiple 2D images of theobject can be captured by the camera 145 from different viewpoints(e.g., top view, bottom view, side view from different angles, etc.).Preferably, sufficient 2D images are captured such that the imagescollectively cover the entire surface area of the object, whileminimizing cost. In some embodiments, the 2D texture of the object canbe interpolated from a sparse number of images.

The 2D images can include still image data, a frame from a video stream,and/or video data. The images can also include data related to theproperties of the optical sensor (e.g., focal length, distortion,compression scheme, etc.) when the images are captured.

As shown in FIG. 1, camera 145 is used to capture multiple 2D images ofthe object 150 from multiple viewpoints (e.g., positions, orientations,distance, perspectives, etc.). In some embodiments, the 2D images areuploaded to a computing device (such as computing device 155) beforesending to the object modeling system 100.

After obtaining the 3D model and 2D images of the object (e.g., theobject 150), the object modeling manager 105 of the object modelingsystem 100 uses different modules of the system 100 to process the modeland images in order to produce the object-image alignment data for theobject. The object-image alignment data can be stored in the database135 and/or sent to an external device (such as mobile device 175) to beused for recognizing and tracking real-world objects.

In some embodiments, the object modeling system 100 takes the 3D modeland generates multiple silhouettes of the object via the silhouettegeneration module 115. Each silhouette of a 3D object represents ageometry of the object without including any details as to the textureand color of the appearance of the object. In some embodiments, thesilhouette produced by the silhouette generation module 115 comprises acollection of edge points that corresponds to the edges and contour ofthe three-dimensional geometry of the object. FIG. 1 illustrates anexample silhouette 160 that the silhouette generation module 115produced based on the 3D model of the object 150. As shown, thesilhouette 160 includes multiple edge points 165 that outline the edgesand contour of the object 150.

Each edge point includes a 3D point (can be represented by a set ofcoordinates in a 3D space) within the 3D model such that each edge pointcan be associated with a 3D location on the 3D model.

In addition, the multiple silhouettes of the object are generated by thesilhouette generation module 115 based on different perspectiveviewpoints (e.g., top view, bottom view, side view from differentangles). In some of these embodiments, each silhouette is generatedbased on a perspective viewpoint that corresponds to a particular cameraviewpoint from which a 2D image of the object is captured.

The object modeling manager 105 on some embodiments then sends thesilhouettes produced by the silhouette generation module 115 and the 2Dimages of the object to the alignment module 125. In some embodiments,the alignment module 125 makes associations between each silhouette andeach 2D image based on the viewpoints on which they are based and alignseach silhouette with the associated 2D image. For example, a top viewsilhouette of an object will be associated with a top view 2D image ofthe object, a bottom view silhouette of an object will be associatedwith a bottom view 2D image of the object, etc. In some embodiments, thesize of the image and/or the silhouette has to be adjusted in order toproduce a perfect alignment.

It is contemplated that the captured 2D images might not accuratelyportray the object that they represent due to different factors duringthe capturing of the images. In other words, the images might show adistorted version of the object. Some of the distortions are caused bythe camera equipment being used to capture the images (e.g., opticaldistortion from a focal length of the lens, etc.). In some embodiments,the alignment module 125 performs a set of distortion eliminationalgorithms on the 2D images to eliminate some of the distortions withthe images so that they can be better aligned with the silhouettes.

In other embodiments, instead of adjusting the 2D images, the alignmentmodule 125 adjusts (e.g., distorts) the silhouettes to align with the 2Dimages.

In some embodiments, aligning a silhouette with a 2D image requiresaligning edge pixels (pixels representing the edge of the object in theimage) within the image to at least some of the edge points. Thealignment of the silhouette and the associated 2D image allows one tocorrespond any point (i.e., location) on the 2D image to a 3D point(i.e., location) on the 3D model (and vice versa).

In some embodiments, the descriptor module 120 also derives at least onedescriptor from the 2D images for the object. A descriptor comprisesdata that describes a recognizable and distinctive feature that appearson the surface of the object (e.g., a logo, a mark, a design, a pattern,facial feature, etc.). In our example, a descriptor for the object 150can comprise data that describes a logo 170 (or a portion thereof) thatappears on the surface of the object 150 or a color scheme on theexterior surface of the object 150. The descriptor can also representanything else that can be used to distinguish the object from otherobjects.

First, the descriptor module 120 identifies distinctive features of theobject, based on the 2D images. Different embodiments use differenttechniques to locate these distinctive features on the object. In someembodiments, the descriptor module 120 uses one or more featureextraction algorithms to identify the distinctive features. Some of themost common feature extraction algorithms that can be used to derivedescriptors from 2D images include: scale-invariant feature transform(SIFT), DAISY, FAST BRISK, and others. These different techniques can beused to identify distinctive features such as faces, logos, marks,signs, patterns, etc. from the images. One should appreciate that morethan one type of descriptor could be derived.

Once the distinctive features are identified, the descriptor module 120generates a descriptor for each identified feature. In some embodiments,the descriptor includes at least one of the following types ofdescriptors: a SIFT descriptor, a DAISY descriptor, a FREAK descriptor,and a FAST descriptor.

In some embodiments, the descriptor module 120 performs additionalpre-processes to the 2D images to prepare for the generation of thedescriptors. Specifically, the descriptor module 120 of some embodimentsremoves the luminance dimension from each pixel of the image such thatall pixels in the image are left with only hue information. Since mostdigital images are represented in a color space that does not include aluminance dimension (e.g., RGB color space, YCrCb color space, etc.), toperform this luminance removal, the descriptor module 120 firsttransform the representation of the image's pixel information from itsnative (original) color space to a color space that includes luminanceas one of its dimensions (e.g., HSL or HSV color space). Once the imageis represented in the new color space, the descriptor module 120 caneasily remove luminance by ignoring (or taking away) the luminance valueof each pixel.

One benefit from removing the luminance value in this process is that iteliminates the optical deltas of the appearance of the images created byhaving different lighting conditions when the images were captured. Thisensures that the descriptors derived from two different images on thesame object captured under different lighting conditions will beidentical for matching purpose.

After removing the luminance dimension from the pixels, the descriptormodule 120 can derive descriptors at different locations on the images.As mentioned above, the descriptor module 120 uses one or more of thefeature extraction algorithms to determine locations where distinctivefeatures exist on the object. For example, on the images of the object150, the feature extraction algorithm can determine that the logo 170 isa distinct feature. Based on the resulting feature list, the descriptormodule 120 will generate a descriptor for each of the feature.

To generate a descriptor, the descriptor module 120 of some embodimentsfirst selects a target pixel at the location of the image where thedistinctive feature exists and then generating a difference vectorbetween the target pixel and each of several of its neighboring pixelsin the image. In the given example, the descriptor module 120 can selecta target pixel that composes the logo 170. The descriptor module 120then identifies several other pixels that neighbor the target pixel. Theneighboring pixels can include pixels that are directly adjacent to thetarget pixel or in close proximity of the target pixel. In someembodiments, the descriptor module 120 of some embodiments generates thedifference vectors for the target pixels based on the differences in huevalues between the target pixel and its neighboring pixels. Preferably,the descriptor module 120 generates at least three difference vectorsfor the target pixel, each based on the difference value between thetarget pixel and a different neighboring pixel. Even more preferably,the descriptor module 120 generates at least eight difference vectorsfor the target pixel. One should appreciate the forgoing descriptionrepresents on possible technique for generating a descriptor.

In some of these embodiments, the descriptor module 120 further computesa composite vector using the multiple difference vectors generated forthe target pixel. The composite vector for that target pixel becomesdata that is part of the descriptor and can be used subsequently forcomparing with other composite vectors in order to determine if anobject that appears in a new image is identical to the target object.More details on how descriptors are derived from an image can be foundin U.S. Pat. No. 6,711,293 issued to David G. Lowe, titled “Method andApparatus for identifying Scale Invariant Features in an Image and Useof Same for Locating and Object in an Image,” filed Mar. 6, 2000.

After generating the descriptors for the object based on the 2D images,the object modeling manager 105 sends the data to the alignment module125 to compile a set of feature points that correspond to thedescriptors for the object. Each feature point for a descriptorcomprises location data that represents a location on a 2D image and alocation on the 3D model of the descriptor. In the given example, thedescriptor module 120 has generated a descriptor corresponding to thelogo 170 using the above-described method. Thus, the alignment module125 compiles at least a feature point for the logo descriptor. Thefeature point includes data that represents a location of the logo'starget pixel on the 2D image, and also a location of the logo on the 3Dmodel. In some embodiments, the alignment module 125 identifies thecorresponding location on the 3D model based on the 3D silhouette thatis aligned with the 2D image.

The object modeling manager 105 then packages the feature points, thedescriptors, the 2D images, and optionally the 3D model of the objectinto an object-image alignment packet for the object. In someembodiments, the object modeling manager 105 stores the object-imagealignment packet in the object-image alignment packets database 135. Inother embodiments, the object modeling manager 105 also sends theobject-image alignment packets to remote devices, such as mobile device175 over a network (e.g., the Internet, a LAN, etc.).

FIG. 2 illustrates a process 200 for generating object-image alignmentdata for object recognition according to some embodiments of theinventive subject matter. The process 200 will be described below byreference to FIGS. 3 and 4. The process 200 begins with generating (atstep 205) a digital 3D model of a real-world object, such as a bottle ora suitcase. The process 200 then generates (at step 210) multiplesilhouettes of the object based on the 3D model. As mentioned above,each of the multiple silhouettes of the object represents the shape ofthe object from a different perspective viewpoint. Preferably, theprocess 200 generates sufficient silhouettes to cover all perspectiveviewpoints of the object.

In addition to the 3D model and silhouettes, the process 200 alsoobtains (at step 215) multiple 2D images of the object. Similar to thesilhouettes, each of the 2D images represents a different cameraviewpoint of the object, dependent of the location of the cameracapturing the object with respect to the object. The process 200 thenregisters (at step 220) each 2D image to be associated with acorresponding silhouette. Preferably, each associated pair of 2D imageand silhouette share the same viewpoint of the object.

After pairing each 2D image with a silhouette, the process 200 compiles(at step 225) alignment points that connect the 2D images to theirassociated silhouettes. Referring to FIG. 3, a 3D model 305 of asuitcase has been generated using conventional methods (e.g., coatingthe suitcase with a non-reflective paint and using a laser scan toobtain the structure of the suitcase, etc.). As shown, the 3D model 305includes different points, such as points 345-370, that represents acollection of points in a three-dimensional space connected by variousgeometric entities (shown as lines forming rectangles in this figure,but can also includes other types of geometries).

Multiple silhouettes can be generated from the 3D model 305. In thisexample, a silhouette 310 of the 3D model 305 is generated based on afront perspective view of the suitcase 3D model 305. Although not shownin this figure, additional silhouettes that are based on otherviewpoints of the 3D model 305 can also be generated from the 3D model305 by the object modeling system 100. As shown, the silhouette 310appears like a shadow as it represents only a geometry of the suitcasefrom a single viewpoint, and does not include any details as to thecolor and surface texture of the suitcase. The silhouette 310 alsoincludes multiple edge points, such as edge points 315-340, thatoutlines the edges and contour of the silhouette 310. It is noted thatedge points 315-340 only represents a subset of possible edge points forsilhouette 310. Preferably, the edge points for the silhouette 310should outline all (or a majority portion) of the edges and contour ofthe silhouette 310.

Preferably, each of the generated edge points 315-340 corresponds to a3D point on the 3D model 305, where the corresponding edge point and 3Dpoint represent the same location of the real-world object. For example,edge point 315 corresponds to 3D point 345, edge point 320 correspondsto 3D point 350, edge point 325 corresponds to 3D point 355, edge point330 corresponds to 3D point 360, edge point 335 corresponds to 3D point365, and edge point 340 corresponds to 3D point 370. These correspondingedge points and 3D points enable one to identify a location on the 3Dmodel 305 given a location on the silhouette.

In addition to the 3D model 305, FIG. 3 also illustrates that a 2D image380 has been generated for the suitcase. In this example, the 2D image380 represents a front perspective viewpoint of the suitcase.Preferably, multiple 2D images that represent different viewpoints ofthe suitcase are generated by the object modeling system 100. Once the2D images are generated, the object modeling system 100 pairs eachsilhouette with a 2D image that shares the same or substantially similarviewpoints. In this example, silhouette 310 of the suitcase is paired(associated) with 2D image 380 because they represent the same object(the suitcase) from a similar (front) perspective viewpoint. Uponpairing each silhouette to its corresponding 2D image, the objectmodeling system 100 align each silhouette with its paired 2D image. Asmentioned above, the silhouette and the associated 2D image might notalign perfectly due to different sizing, distortions, etc. As such, someimage processing might be performed on the silhouette, or the 2D image,or both in order to better align the two. As shown in this figure, thesilhouette 310 is shrunk in size to become silhouette 375 so that it canbe better aligned with the suitcase appear in the 2D image 380.

FIG. 3 illustrates that the shrunk silhouette 375 is perfectly alignedwith the 2D image 380 in an alignment image 385. In some embodiments,the alignment process includes deriving alignment points. In someembodiments, an alignment point comprises an edge point of thesilhouette and also an identification of a corresponding pixel in the 2Dimage. In this example, an alignment point can be generated for the edgepoint 315 on the silhouette 310 and includes a pixel on the 2D image 380that represents the lower left corner of the suitcase. Similarly, analignment point can be generated for the edge point 335 and includes apixel on the 2D image 380 that represents the upper right corner of thesuitcase. Because of the associations between edge points on thesilhouette 310 and locations on the 3D model 305, the alignment pointsallow one to associate any pixel on the 2D image 380 to a location onthe 3D model 305 (e.g., by calculating the distance (vector) between thepixel on the 2D image to the different pixels that have been associatedwith the edge points on the silhouette 310.

Referring back to FIG. 2, after compiling the alignment points, theprocess 200 derives (at step 230) a set of descriptors for each of the2D images. FIG. 4 illustrates a set of descriptors being generated fromthe 2D image 380 of the suitcase. To generate the descriptors, theobject modeling system is programmed to first identify a set of features(e.g., local features, global features, a combination of both local andglobal features, etc.) on the 2D image 380. In one example, the objectmodeling system 100 can use an image recognition algorithm such asscale-invariant feature transform (SIFT; see U.S. Pat. No. 6,711,293titled “Method and apparatus for identifying scale invariant features inan image and use of same for locating an object in an image” filed Mar.6, 2000) to detect and describe local features (as descriptors) inimages.

The identified features can include an area of the image 380 around theedges and/or corners of a detected object within the image 380. Forexample, the image 380 of the suitcase can have a descriptor thatdescribes a part of the handle on the suitcase, a part of the buckle, apart of the belt, a part of the “legs”, etc. In this example, the objectmodeling system 100 has identified five features 405-425 within theimage 380 of the suitcase to form the descriptor set 430-450.Preferably, the five features 405-425 represent unique features of thecaptured suitcase. For each identified feature, the object modelingsystem 100 is programmed to derive a descriptor (e.g., SIFT descriptors,Histogram of Gradients, etc.). The descriptor essentially characterizesone or more aspects (e.g., color aspect, gradient aspect, contrastaspect, etc.) of the corresponding identified feature.

Referring back to FIG. 2, once a set of descriptors is generated for the2D image, the process 200 then generates (at step 235) an object-imagealignment packet based on the alignment points and the descriptors. Asmentioned above, the object-image alignment packet includes eachgenerated alignment point and its corresponding descriptor thatdescribes the feature located at the alignment point.

The generated object-image alignment packet can give rise to manypractical usages, including recognizing and tracking objects. Forexample, the object-image alignment packet is particularly advantageouswhen recognizing and/or tracking featureless objects, such as a logo. Alogo is usually a two-dimensional image without much of a texture. Assuch, it is much easier to map a silhouette to an image of the logo.

In another example, once a recognized object is identified, the systemcan mask the image or video frame using the silhouette that has beenaligned with the digital representation of the object (e.g., the imageor the video frame), and superimpose (or overlay) a green screen on theremaining portion of the image/video frame. This enables virtual realityand augmented reality by easily taking the portion of the image thatrepresents the object out of the image/video frame and put it in anotherdigital media.

In another use case, the silhouettes associated with an object can beused as the basis for recognizing and tracking strategy. For example,once the object is recognized in an image or a video frame, the systemcan use the associated silhouette of the object (e.g., by overlaying thesilhouette on top of the digital representation of the object in theimage/video frame) to determine if there is movement of the object fromframe to frame. In some of these embodiments, even micro motion of theobject can be detected using the silhouette, where conventional objectrecognition/tracking technology could not do.

It should be apparent to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

What is claimed is:
 1. A computer-based method of generatingobject-image alignment data, the method comprising: obtaining, via animage processing engine, a digital three-dimensional (3D) model of anobject; obtaining, via the image processing engine, a set oftwo-dimensional (2D) images of the object from a plurality of image viewpoints; generating, via the image processing engine, a silhouette of theobject from the digital 3D model where the silhouette comprises acollection of edge points associated with edges of the digital 3D modelfrom a silhouette point of view; registering, via the image processingengine, at least one image from the set of 2D images with the silhouettebased an image view point associated with the at least one image and thesilhouette point of view; deriving, via the image processing engine, atleast one descriptor from the at least one image; compiling, via theimage processing engine, alignment points based on the at least onedescriptor and the collection of edge points, the alignment pointsincluding at least some of the edge points; and generating, via theimage processing engine, an object-image alignment packet by packagingthe at least one image with alignment points.
 2. The computer-basedmethod of claim 1, wherein the object-image alignment packet comprises akey frame packet.
 3. The computer-based method of claim 1, wherein thecollection of points comprises 3D points.
 4. The computer-based methodof claim 3, wherein the collection of points comprises relative 3Dcoordinates to a camera point of view.
 5. The computer-based method ofclaim 1, wherein the alignment points comprises 3D coordinates to acamera point of view.
 6. The computer-based method of claim 1, whereinthe step of obtaining the digital 3D model of the object includes laserscanning the object.
 7. The computer-based method of claim 1, whereinthe step of obtaining the digital 3D model of the object includesobtaining CAD data representing the object.
 8. The computer-based methodof claim 1, wherein the step of obtaining the digital 3D model of theobject includes obtaining a game engine asset representing the object.9. The computer-based method of claim 1, wherein the step of obtainingthe set of 2D images of the object includes a capturing image data ofthe object via an optical sensor.
 10. The computer-based method of claim9, wherein image data comprises still image data.
 11. The computer-basedmethod of claim 10, wherein the still image data comprises a frame froma video stream.
 12. The computer-based method of claim 9, wherein theimage data comprises video data.
 13. The computer-based method of claim1, wherein the step of obtaining the set of 2D images of the objectincludes a capturing image data of the object via an optical sensor. 14.The computer-based method of claim 1, wherein the step of registering atleast one image from the set of 2D images with the silhouette includingaligning image data of the object within the at least image to at leastsome of the edge points.
 15. The computer-based method of claim 14,wherein the step of aligning the image data includes aligning edgepixels within the image data of the object to at least some of the edgepoints.
 16. The computer-based method of claim 1, wherein the at leastone descriptor comprises an image descriptor.
 17. The computer-basedmethod of claim 16, wherein the image descriptor comprises at least oneof the following types of descriptors: a SIFT descriptor, a DAISYdescriptor, a FREAK descriptor, and a FAST descriptor.
 18. Thecomputer-based method of claim 1, further comprising sending theobject-image alignment packet to an electronic device.
 19. Thecomputer-based method of claim 18, further comprising sending theobject-image alignment packet over a network.
 20. The computer-basedmethod of claim 1, wherein the object-image alignment packet includes atleast one of the following: a normal vector, an orientation, and sensormetadata.